Code Challenge 51 – Analyse NBA Data with SQL/sqlite3 – Review

In this article we review last week’s Analyse NBA Data with SQL/sqlite3 code challenge.

Our solution

Check out our solution for this challenge.

Some learnings:

• Use cursor.executemany to bulk insert records.

• We were using cursor.fetchall but to get one record/row you can use fetchone (thanks @clamytoe)

• Practice GROUP BY (year_with_most_drafts)

• Simple SQLite arithmetic (games/active AS games_per_year)

• Probably don’t need CAST if you add types to DB columns (looking at other PRs!)

Community solutions

Check out solutions PR’d by our community.

Some learnings taken from these Pull Requests:

• Refreshed SQL. Learned about sqlite command line. Learned PyCharm DataSource integration and querying. Refreshed git commands.

• I used this challenge as a chance to experiment with Jupyter notebook to help visualize the data

Read Code for Fun and Profit

You can look at all submitted code here and/or pulling our Community branch.

Other learnings we spotted in Pull Requests week: itertools, difflib / similarity measures, collections, pytest and patch.

Thanks to everyone for your participation in our blog code challenges!

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Keep Calm and Code in Python!

— Bob and Julian